• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.5
• /
• pp.347-356
• /
• 2003

Camera pose and scene geometry estimation from video sequences is widely used in various areas such as image composition. Structure and motion recovery based on the auto calibration algorithm can insert synthetic 3D objects in real but un modeled scenes and create their views from the camera positions. However, most previous methods require bundle adjustment or non linear minimization process [or more precise results. This paper presents a new auto' calibration algorithm for video sequence based on two steps: the one is key frame selection, and the other removes the key frame with inaccurate camera matrix based on an absolute quadric estimation by LMedS. In the experimental results, we have demonstrated that the proposed method can achieve a precise camera pose estimation and scene geometry recovery without bundle adjustment. In addition, virtual objects have been inserted in the real images by using the camera trajectories.

• Korean Journal of Remote Sensing
• /
• v.32 no.6
• /
• pp.647-656
• /
• 2016

Growth of UAV technology leads to expansion of UAV image applications. Many UAV image-based applications use a method called incremental bundle adjustment. However, incremental bundle adjustment produces large computation overhead because it attempts feature matching from all image pairs. For efficient feature matching process we have to confine matching only for overlapping pairs using exterior orientation parameters. When exterior orientation parameters are not available, we cannot determine overlapping pairs. We need another methods for feature matching candidate constitution. In this paper we compare matching candidate constitution methods without exterior orientation parameters, including partial feature matching, Bag-of-keypoints, image intensity method. We use the overlapping pair determination method based on exterior orientation parameter as reference. Experiment results showed the partial feature matching method in the one with best efficiency.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.9 no.1
• /
• pp.75-82
• /
• 1991

The objective of this paper is to investigate the optimal Robust estimation and scale estimator that could be used to treat the gross errors in a self calibrating bundle adjustment. In order to test the variability in performance of the different weighting schemes in accurately detecting gross error, five robust estimation methods and three types of scale estimators were used. And also, two difference control point patterns(high density control, sparse density control) and three types of gross errors(4$\sigma o$, 20$\sigma o$, 50$\sigma o$) were used for comparison analysis. As a result, Anscombe's robust estimation produced the best results in accuracy among the robust estimation methods considered. when considering the scale estimator about control point patterns, It can be seen that Type II scale estimator provided the best accuracy in high density control pattern. On the other hand, In the case of sparse density control pattern, Type III scale estimator showed the best results in accuracy. Therefore it is expected to apply to robustified bundle adjustment using the optimal scale estimator which can be used for eliminating the gross error in precise structure analysis.

• Korean Journal of Remote Sensing
• /
• v.19 no.6
• /
• pp.447-456
• /
• 2003

This study investigated the method for obtaining 3-dimensional terrain information on inaccessable areas by evaluating geometric accuracy of the EOC pass image and scene image acquired from the KOMPSAT-1 satellite. For this purpose, the following four experiments were conducted to evaluate the accuracy of the KOMPSAT-1 EOC satellite data. 1) Calculation of ground coordinates by using ancillary data and image coordinates on Level 1R that were processed by the pre-processing system of KOMPSAT-1. 2) Calculation of 3-dimensional ground coordinates from the ground coordinates of stereo images calculated by using ancillary data, based on space intersections. 3) Execution of bundle adjustment by using GCP (Ground Control Point) extracted in a part of the stereo pass image (KOMPSAT-1 EOC, 1 scene size); and then, evaluation of the ground coordinates from the calculated exterior orientation. 4) Evaluation of accuracy by applying the exterior orientation calculated from 3) To the whole pass image.

• Journal of Cadastre & Land InformatiX
• /
• v.51 no.2
• /
• pp.185-198
• /
• 2021

Researches using UAVs are being actively conducted in the field of quickly constructing 3D spatial information in small areas. In this study, without using ground control points, a stereo camera was mounted on a UAV to collect images and quickly construct three-dimensional positions through image matching, bundle adjustment, and the determination of a scale factor. Through the experiment, when bundle adjustment was performed using stereo constraints, the root mean square error was 1.475m, and when absolute orientation was performed in consideration of a scale, it was found to be 0.029m. Through this, it was found that when using the data processing method of a UAV equipped with a stereo camera proposed in this study, high-accuracy 3D spatial information can be constructed without using ground control points.

• Korean Journal of Remote Sensing
• /
• v.26 no.5
• /
• pp.593-603
• /
• 2010

Most spatial data acquisition systems employing a set of frame cameras may have suffered from their small fields of view and poor base-distance ratio. These limitations can be significantly reduced by employing an omni-directional camera that is capable of acquiring images in every direction. Bundle Block Adjustment (BBA) is one of the existing georeferencing methods to determine the exterior orientation parameters of two or more images. In this study, by extending the concept of the traditional BBA method, we attempt to develop a mathematical model of BBA for omni-directional images. The proposed mathematical model includes three main parts; observation equations based on the collinearity equations newly derived for omni-directional images, stochastic constraints imposed from GPS/INS data and GCPs. We also report the experimental results from the application of our proposed BBA to the real data obtained mainly in urban areas. With the different combinations of the constraints, we applied four different types of mathematical models. With the type where only GCPs are used as the constraints, the proposed BBA can provide the most accurate results, ${\pm}5cm$ of RMSE in the estimated ground point coordinates. In future, we plan to perform more sophisticated lens calibration for the omni-directional camera to improve the georeferencing accuracy of omni-directional images. These georeferenced omni-directional images can be effectively utilized for city modelling, particularly autonomous texture mapping for realistic street view.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.27 no.5
• /
• pp.585-597
• /
• 2009

This research aims to develop a registration method to remove the geometric inconsistency between aerial images and LIDAR data acquired from an airborne multi-sensor system. The proposed method mainly includes registration primitives extraction, correspondence establishment, and EOP(Exterior Orientation Parameters) adjustment. As the registration primitives, we extracts planar patches and intersection edges from the LIDAR data and object points and linking edges from the aerial images. The extracted primitives are then categorized into horizontal and vertical ones; and their correspondences are established. These correspondent pairs are incorporated as stochastic constraints into the bundle block adjustment, which finally precisely adjusts the exterior orientation parameters of the images. According to the experimental results from the application of the proposed method to real data, we found that the attitude parameters of EOPs were meaningfully adjusted and the geometric inconsistency of the primitives used for the adjustment is reduced from 2 m to 2 cm before and after the registration. Hence, the results of this research can contribute to data fusion for the high quality 3D spatial information.

• Journal of Positioning, Navigation, and Timing
• /
• v.2 no.1
• /
• pp.81-89
• /
• 2013

Many of the current visual odometry algorithms suffer from some extreme limitations such as requiring a high amount of computation time, complex algorithms, and not working in urban environments. In this paper, we present an approach that can solve all the above problems using a single camera. Using a planar motion assumption and Ackermann's principle of motion, we construct the vehicle's motion model as a circular planar motion (2DOF). Then, we adopt a 1-point method to improve the Ransac algorithm and the relative motion estimation. In the Ransac algorithm, we use a 1-point method to generate the hypothesis and then adopt the Levenberg-Marquardt method to minimize the geometric error function and verify inliers. In motion estimation, we combine the 1-point method with a simple least-square minimization solution to handle cases in which only a few feature points are present. The 1-point method is the key to speed up our visual odometry application to real-time systems. Finally, a Bundle Adjustment algorithm is adopted to refine the pose estimation. The results on real datasets in urban dynamic environments demonstrate the effectiveness of our proposed algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.19 no.3
• /
• pp.283-290
• /
• 2001

This paper demonstrates the performance of industrial precise measurement using the digital close-range photograrmmetric system based on a off-the-shelf CCTV-type CCD camera. The system was constructed with a CCD camera and a PC with a frame grabber, coupled with digital image mensuration and self-calibrating bundle adjustment techniques. An artificial fish reef with cubic shape was taken as an object for the application test of the system and the digital images were acquired on multi-station convergent network around the object. The geometric calibration of the CCD camera and the phototriangulation of the entire surface of the object was carried out simultaneously by means of self-calibrating bundle adjustment technique. Also the system comprising a high resolution still-video camera Kodak DCS, which high accuracy potential has been already established, were employed in similar network condition. Then the results from two different camera systems were compared in the accuracies of phototriangulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.13 no.1
• /
• pp.95-106
• /
• 1995

The purpose of this study is to analyse the accuracy of non-metric photos by close-range photogrammetry. Close-range photogrammetry using non-metric photos is 'economical and convenient to handle, but it is insufficient of study on accuracy. To execute this study, first, the terrain model was made and then taken photographs of this model with metric and non-metric cameras. The Bundle adjustment and the Direct linear transformation methods are used for the analysis close-range photogrammetry. The results of the analysis showed that the Bundle adjustment method is a appropriate method for the analysis of the non-netric photo. Therefore, we concluded that the accuracy of the non-metric photo by close-range photogrammetry is applicability for the photogrammetry.